Author: Jason Bell ISBN-10: 1118889061 Year: 2014 Pages: 408 Language: English File size: 8. Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate machine learning applications pdf presented techniques into their own work as they follow along. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science.
Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science,Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper. Reproduction of site books is authorized only for informative purposes and strictly for personal, private use.
My research interests lie in the areas of machine learning, computational advertising and computer vision. Classifiers that I have developed have been deployed on millions of devices around the world and have protected them from viruses and malware. Machine learning: Machine learning for the Internet of Things, extreme classification, recommender systems, multi-label learning, supervised learning. Computer vision: Image search, object recognition, text recognition, texture classification. Computational advertising: Bid phrase suggestion, query recommendation, contextual matching.
Joining my group: I am looking for full time PhD students at IIT Delhi and Research Fellows at Microsoft Research India to work with me on research problems in supervised machine learning, extreme classification, recommender systems and resource constrained machine learning for the Internet of Things. Projects: Unfortunately, I am unable to supervise projects of students outside IIT Delhi. If you are an external student and would like to work with me then the best way would be to join IIT Delhi’s PhD programmes or apply for a Research Fellowship at MSR India. Internships: If you are a PhD student looking to do an internship with me then please e-mail me directly. I have only one or two internship slots and competition is stiff so please apply early. Please do not apply to me or e-mail me about internships if you are not a PhD student as I will not be able to respond to you.
Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising. Extreme multi-label learning with label features for warm-start tagging, ranking and recommendation. Resource-efficient machine learning in 2 KB RAM for the Internet of Things. ProtoNN: Compressed and accurate kNN for resource-scarce devices. Sparse local embeddings for extreme multi-label classification.
In Advances in Neural Information Processing Systems, Montreal, Canada, December 2015. FastXML: A fast, accurate and stable tree-classifier for extreme multi-label learning. Active learning for sparse Bayesian multi-label classification. On p-norm path following in multiple kernel learning for non-linear feature selection.
Local deep kernel learning for efficient non-linear SVM prediction. Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages. SPG-GMKL: Generalized multiple kernel learning with a million kernels. Efficient max-margin multi-label classification with applications to zero-shot learning. Learning to re-rank: Query-dependent image re-ranking using click data. Multiple kernel learning and the SMO algorithm. In Advances in Neural Information Processing Systems, Vancouver, B.
Large scale max-margin multi-label classification with priors. A statistical approach to material classification using image patch exemplars. More generality in efficient multiple kernel learning. Locally invariant fractal features for statistical texture classification. Computer aided generation of stylized maps. A statistical approach to texture classification from single images. DPhil Thesis, University of Oxford, October 2004.
Estimating illumination direction from textublue images. Texture classification: Are filter banks necessary? Classifying materials from images: to cluster or not to cluster? Classifying images of materials: Achieving viewpoint and illumination independence. Statistical Learning MOOC covering the entire ISL book offered by Trevor Hastie and Rob Tibshirani.